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Advanced Statistical Methods for Complex Data
Thu, April 8, 2:00 to 3:00pm EDT (2:00 to 3:00pm EDT), Division D, Division D - Section 2 Roundtable SessionsSession Type: Roundtable Session
Abstract
Innovations have accumulated large amounts of complex data and allowed researchers to consider increasingly sophisticated statistical models. This session addresses two types of such models: the first one focuses on network modeling of complex human behaviors with or without missing data, and the second on evaluation of variable selection methods when the assumptions do not hold or when missing data exist.
Sub Unit
Chair
Papers
Identifying Friendship Layers in Peer Interactions on Campus From Spatial Co-Occurrences - Quan Nguyen, University of Michigan - Ann Arbor; Christopher Brooks, University of Michigan; Oleksandra Poquet, University of South Australia
Monte Carlo Evaluation of the Linearity and Gaussian Error Assumptions for Lasso - Christina Witta Gillespie, Valencia College
Variable Selection by Regularized Multiple-Indicators Multiple-Causes Modeling With Missing Data: A Monte Carlo Simulation - Minjeong Rho, Korea National University of Education; Jin Eun Yoo, Korea National University of Education
Missing Data Handling in Weighted Social Network Analyses - Nathan Abe, The Energy Authority; Elizabeth A. Sanders, University of Washington
The Ecology of Human Behavior: A Network Perspective - Rafael Quintana, University of Kansas